Glonek G F V, Solomon P J
School of Applied Mathematics, The University of Adelaide, Adelaide, SA 5005, Australia.
Biostatistics. 2004 Jan;5(1):89-111. doi: 10.1093/biostatistics/5.1.89.
Microarrays are powerful tools for surveying the expression levels of many thousands of genes simultaneously. They belong to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. There are myriad sources of uncertainty in microarray experiments, and rigorous experimental design is essential for fully realizing the potential of these valuable resources. Two questions frequently asked by biologists on the brink of conducting cDNA or two-colour, spotted microarray experiments are 'Which mRNA samples should be competitively hybridized together on the same slide?' and 'How many times should each slide be replicated?' Early experience has shown that whilst the field of classical experimental design has much to offer this emerging multi-disciplinary area, new approaches which accommodate features specific to the microarray context are needed. In this paper, we propose optimal designs for factorial and time course experiments, which are special designs arising quite frequently in microarray experimentation. Our criterion for optimality is statistical efficiency based on a new notion of admissible designs; our approach enables efficient designs to be selected subject to the information available on the effects of most interest to biologists, the number of arrays available for the experiment, and other resource or practical constraints, including limitations on the amount of mRNA probe. We show that our designs are superior to both the popular reference designs, which are highly inefficient, and to designs incorporating all possible direct pairwise comparisons. Moreover, our proposed designs represent a substantial practical improvement over classical experimental designs which work in terms of standard interactions and main effects. The latter do not provide a basis for meaningful inference on the effects of most interest to biologists, nor make the most efficient use of valuable and limited resources.
微阵列是同时检测成千上万基因表达水平的强大工具。它们属于新的基因组技术，在生物学、农业和制药科学领域有着重要应用。微阵列实验存在无数不确定因素，严谨的实验设计对于充分发挥这些宝贵资源的潜力至关重要。即将开展cDNA或双色点阵微阵列实验的生物学家经常问到的两个问题是：“哪些mRNA样本应在同一张玻片上进行竞争性杂交？”以及“每张玻片应重复多少次？”早期经验表明，虽然经典实验设计领域能为这个新兴的多学科领域提供很多帮助，但仍需要适应微阵列背景特定特征的新方法。在本文中，我们提出了析因实验和时间进程实验的最优设计，这是微阵列实验中经常出现的特殊设计。我们的最优性标准是基于可允许设计的新概念的统计效率；我们的方法能够根据生物学家最感兴趣的效应的可用信息、实验可用的阵列数量以及其他资源或实际限制（包括mRNA探针量的限制）来选择高效设计。我们表明，我们的设计优于既低效又包含所有可能直接成对比较的流行参考设计。此外，我们提出的设计相对于基于标准相互作用和主效应的经典实验设计有实质性的实际改进。后者既没有为生物学家最感兴趣的效应提供有意义的推断基础，也没有最有效地利用宝贵且有限的资源。
